Single-loop feedback controllers (“controllers”) are commonly employed to maintain temperature, humidity, pressure, and flow rates for heating, ventilating, and air-conditioning equipment. For example, in an air conditioning system, a controller may be used to control the flow of chilled water through a cooling coil. In such a system, the controller adjusts the water flow rate based on a feedback signal indicative of the temperature of the air discharged from the coil (the “controlled variable”). The feedback signal is generated by a sensor disposed to monitor the controlled variable.
The object of such controllers is to control the system in such a way as to maintain the controlled variable, as sensed by the feedback signal, at a desired level (the “setpoint”). For example, the controller of an air conditioning system attempts to maintain the temperature of the air discharged from the system at a specific level. When the actual temperature of the discharged air deviates from the desired temperature, the controller must appropriately adjust the flow of the chilled water to bring the actual air temperature back in line with the desired air temperature. Thus, if the feedback signal indicates that the actual air temperature is colder than the desired temperature, the controller will cause the flow rate of chilled water to decrease, which will cause the actual temperature of the discharged air to increase. Likewise, if the feedback signal indicates that the actual air temperature is warmer than the desired temperature, the controller will cause the flow rate of chilled water to increase, which will cause the actual temperature of the discharged air to decrease.
An ideal feedback control system would be able to maintain the controlled variable at the setpoint based only on the feedback signal. However, actual feedback control systems require additional inputs known as control parameters. Control parameters are values used by a controller to determine how to control a system based on the feedback signal and the setpoint.
One commonly used method for controlling a closed loop system, known as proportional plus integral control (PI), requires two control parameters: proportional gain and integral time. Since these two control parameters directly affect the performance and stability of a PI controller, it is important to determine the appropriate values of these parameters. However, the appropriate values for these parameters may change over time as the system is used. For example, the dynamics of a process may be altered by heat exchanger fouling, inherent nonlinear behavior, ambient variations, flow rate changes, large and frequent disturbances, and unusual operations status, such as failures, startup and shutdown. The process of adjusting the control parameters of a controller to compensate for such system changes is called retuning. If a controller is not retuned, the control response may be poor. For example, the controlled variable may become unstable or oscillate widely with respect to the setpoint. Thus, to insure adequate performance, controllers should be periodically retuned with new control parameter values.
The various tuning methods which have been developed to determine the appropriate values of the control parameters for PI controllers fall into three general categories. These categories are: manual tuning, auto-tuning, and adaptive control. Manual tuning methods require an operator to run different test or trial and error procedures to determine the appropriate control parameters. Manual tuning methods have the obvious disadvantage of requiring large amounts of operator time and expertise. Auto-tuning methods require an operator to periodically initiate tuning procedures, during which the controller will automatically determine the appropriate control parameters. The control parameters thus set will remain unchanged until the next tuning procedure. While auto-tuning requires less operator time than manual tuning methods, it still requires operator intervention. Further, during the interval between tunings, the controller may become severely out of tune and operate poorly. With adaptive control methods, the control parameters are automatically adjusted during normal operation to adapt to changes in process dynamics. Thus, no operator intervention is required. Further, the control parameters are continuously updated to prevent the degraded performance which may occur between the tunings of the other methods.
There are three main approaches to adaptive control: model reference adaptive control (“MRAC”), self-tuning control, and pattern recognition adaptive control (“PRAC”). The first two approaches, MRAC and self-tuning, rely on system models which are generally quite complex. The complexity of the models is necessitated by the need to anticipate unusual or abnormal operating conditions. Specifically, MRAC involves adjusting the control parameters until the response of the system to a command signal follows the response of a reference model. Self-tuning control involves determining the parameters of a process model on-line and adjusting the control parameters based upon the parameters of the process model.
With PRAC, parameters that characterize the pattern of the closed-loop response are determined after significant setpoint changes or load disturbances have occurred. The control parameters are then adjusted based upon the characteristic parameters of the closed-loop response. Some known pattern recognition adaptive controllers require an operator to enter numerous control parameters before normal operation may begin. The more numerous the operator selected control parameters, the more difficult it is to adjust the pattern recognition adaptive controller for optimal performance, and the longer it takes to prepare the pattern recognition adaptive controller for operation.
Significant advances in the art have been disclosed in commonly owned U.S. Pat. Nos. 5,355,305 and 5,506,768, the entire contents of which are incorporated herein by reference. U.S. Pat. Nos. 5,355,305 and 5,506,768 (the '305 and '768 patents) provide for a pattern recognition adaptive controller with fewer operator-specified control variables than are required by other known pattern recognition adaptive controllers. The '305 and '768 patents further provide for a pattern recognition adaptive controller with improved performance, and particularly one which performs in a near-optimal manner under a large amount of noise. The '305 and '768 patents further provide for a pattern recognition adaptive controller with a variable tune noise band which adjusts automatically to different noise levels in the process. The '305 and '768 patents also provide for a pattern recognition adaptive controller which efficiently controls a process with a reduced number of actuator adjustments, and therefore reduced energy costs, by decreasing oscillations for the controlled variable signal. The '305 and '768 patents further provide for a robust pattern recognition adaptive controller that performs relatively secure control without constraining the values of its parameters to a predetermined range. The '305 and '768 patents also provide for a pattern recognition adaptive controller with reduced resource requirements, and more particularly, which requires less memory and less computational power than previous pattern recognition adaptive controllers.
While the inventions disclosed in U.S. Pat. Nos. 5,355,305 and 5,506,768 represent significant advances in the art, it is desirable to provide a further improved method for automatically adjusting the gain and integral time of proportional-integral controllers based upon patterns that characterize the closed-loop response. In particular, it is desirable to provide a pattern recognition adaptive controller that does not detune when there are periodic load disturbances. It is also desirable to provide a pattern recognition adaptive controller that does not detune when the controller gain is extremely large compared to an optimal value. Finally, it is desirable to provide a pattern recognition adaptive controller that does not detune undersized systems that are started repeatedly.